Book description
- Get an in-depth understanding of all the major machine learning and deep learning algorithms
- Fully appreciate the pitfalls to avoid while building models
- Implement machine learning algorithms in the cloud
- Follow a hands-on approach through case studies for each algorithm
- Gain the tricks of ensemble learning to build more accurate models
- Discover the basics of programming in R/Python and the Keras framework for deep learning
Table of contents
- Cover
- Front Matter
- 1. Basics of Machine Learning
- 2. Linear Regression
- 3. Logistic Regression
- 4. Decision Tree
- 5. Random Forest
- 6. Gradient Boosting Machine
- 7. Artificial Neural Network
- 8. Word2vec
- 9. Convolutional Neural Network
- 10. Recurrent Neural Network
- 11. Clustering
- 12. Principal Component Analysis
- 13. Recommender Systems
- 14. Implementing Algorithms in the Cloud
- Back Matter
Product information
- Title: Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R
- Author(s):
- Release date: June 2018
- Publisher(s): Apress
- ISBN: 9781484235645
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